Overview

Dataset statistics

Number of variables12
Number of observations65281
Missing cells0
Missing cells (%)0.0%
Duplicate rows3657
Duplicate rows (%)5.6%
Total size in memory6.0 MiB
Average record size in memory96.0 B

Variable types

Numeric9
Text1
Categorical2

Alerts

Dataset has 3657 (5.6%) duplicate rowsDuplicates
Discount Amount is highly overall correlated with Sales Amount and 3 other fieldsHigh correlation
List Price is highly overall correlated with Sales Price and 1 other fieldsHigh correlation
Sales Amount is highly overall correlated with Discount Amount and 3 other fieldsHigh correlation
Sales Amount Based on List Price is highly overall correlated with Discount Amount and 3 other fieldsHigh correlation
Sales Cost Amount is highly overall correlated with Discount Amount and 3 other fieldsHigh correlation
Sales Margin Amount is highly overall correlated with Discount Amount and 3 other fieldsHigh correlation
Sales Price is highly overall correlated with List Price and 1 other fieldsHigh correlation
Sales Quantity is highly overall correlated with List Price and 1 other fieldsHigh correlation
Sales Cost Amount is highly skewed (γ1 = 21.01078573)Skewed
Sales Quantity is highly skewed (γ1 = 23.00739577)Skewed
Discount Amount has 1214 (1.9%) zerosZeros

Reproduction

Analysis started2023-07-10 12:54:24.572202
Analysis finished2023-07-10 12:54:38.212060
Duration13.64 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

CustKey
Real number (ℝ)

Distinct615
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10017703
Minimum10000453
Maximum10027583
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.1 KiB
2023-07-10T18:24:38.319957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10000453
5-th percentile10002506
Q110012715
median10019665
Q310023511
95-th percentile10026006
Maximum10027583
Range27130
Interquartile range (IQR)10796

Descriptive statistics

Standard deviation7176.1904
Coefficient of variation (CV)0.0007163509
Kurtosis-0.37137474
Mean10017703
Median Absolute Deviation (MAD)4886
Skewness-0.77019433
Sum6.5396565 × 1011
Variance51497708
MonotonicityNot monotonic
2023-07-10T18:24:38.472528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10025919 2760
 
4.2%
10019194 2752
 
4.2%
10012715 1431
 
2.2%
10012226 1389
 
2.1%
10025025 1143
 
1.8%
10023524 1042
 
1.6%
10020515 1010
 
1.5%
10017638 792
 
1.2%
10022456 741
 
1.1%
10002506 714
 
1.1%
Other values (605) 51507
78.9%
ValueCountFrequency (%)
10000453 329
0.5%
10000455 19
 
< 0.1%
10000456 104
 
0.2%
10000457 19
 
< 0.1%
10000458 10
 
< 0.1%
10000460 120
 
0.2%
10000461 251
0.4%
10000462 3
 
< 0.1%
10000466 123
 
0.2%
10000469 162
0.2%
ValueCountFrequency (%)
10027583 25
 
< 0.1%
10027575 5
 
< 0.1%
10027572 52
 
0.1%
10027560 42
 
0.1%
10027381 108
0.2%
10027370 235
0.4%
10027356 21
 
< 0.1%
10027348 14
 
< 0.1%
10027340 35
 
0.1%
10027119 176
0.3%

Discount Amount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17821
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1855.5748
Minimum-255820.8
Maximum343532.66
Zeros1214
Zeros (%)1.9%
Negative972
Negative (%)1.5%
Memory size510.1 KiB
2023-07-10T18:24:38.620962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-255820.8
5-th percentile18.68
Q1246.04
median441.76
Q3999.76
95-th percentile6353
Maximum343532.66
Range599353.46
Interquartile range (IQR)753.72

Descriptive statistics

Standard deviation9037.0717
Coefficient of variation (CV)4.8702275
Kurtosis379.74222
Mean1855.5748
Median Absolute Deviation (MAD)233.95
Skewness10.841862
Sum1.2113378 × 108
Variance81668664
MonotonicityNot monotonic
2023-07-10T18:24:38.754924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1214
 
1.9%
24.88 103
 
0.2%
606.84 100
 
0.2%
639.82 97
 
0.1%
634.6133 93
 
0.1%
402.7 93
 
0.1%
601.9033 93
 
0.1%
918.1412 88
 
0.1%
385.98 87
 
0.1%
169.36 87
 
0.1%
Other values (17811) 63226
96.9%
ValueCountFrequency (%)
-255820.8 1
 
< 0.1%
-245587.97 1
 
< 0.1%
-238792.73 1
 
< 0.1%
-231837.6 3
< 0.1%
-222564.1 3
< 0.1%
-127176 1
 
< 0.1%
-122088.96 1
 
< 0.1%
-84573.72 1
 
< 0.1%
-81190.77 1
 
< 0.1%
-53626 1
 
< 0.1%
ValueCountFrequency (%)
343532.66 2
< 0.1%
339103.35 1
 
< 0.1%
331487.76 2
< 0.1%
327213.75 1
 
< 0.1%
322454.09 1
 
< 0.1%
210371 4
< 0.1%
202995 4
< 0.1%
191196.5532 2
< 0.1%
189333.9 1
 
< 0.1%
182832.8832 2
< 0.1%

Item
Text

Distinct657
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size510.1 KiB
2023-07-10T18:24:38.987682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length37
Median length32
Mean length21.722278
Min length8

Characters and Unicode

Total characters1418052
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)< 0.1%

Sample

1st rowNationeel Salted Pretzels
2nd rowEbony Prepared Salad
3rd rowThresher Spicy Mints
4th rowBig Time Frozen Cheese Pizza
5th rowRed Spade Turkey Hot Dogs
ValueCountFrequency (%)
canned 6378
 
2.8%
ebony 5460
 
2.4%
cheese 5194
 
2.3%
better 4570
 
2.0%
red 4271
 
1.9%
top 4173
 
1.8%
spade 4161
 
1.8%
high 4138
 
1.8%
best 3480
 
1.5%
nationeel 3328
 
1.4%
Other values (294) 184534
80.3%
2023-07-10T18:24:39.369719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
164406
 
11.6%
e 147096
 
10.4%
o 92467
 
6.5%
a 92276
 
6.5%
n 74074
 
5.2%
i 69460
 
4.9%
t 68733
 
4.8%
r 67352
 
4.7%
l 59835
 
4.2%
s 57796
 
4.1%
Other values (46) 524557
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1013747
71.5%
Uppercase Letter 236243
 
16.7%
Space Separator 164406
 
11.6%
Dash Punctuation 2160
 
0.2%
Other Punctuation 748
 
0.1%
Decimal Number 748
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 147096
14.5%
o 92467
 
9.1%
a 92276
 
9.1%
n 74074
 
7.3%
i 69460
 
6.9%
t 68733
 
6.8%
r 67352
 
6.6%
l 59835
 
5.9%
s 57796
 
5.7%
d 40545
 
4.0%
Other values (16) 244113
24.1%
Uppercase Letter
ValueCountFrequency (%)
B 31428
13.3%
C 30873
13.1%
S 26130
11.1%
T 19374
 
8.2%
F 16239
 
6.9%
M 12522
 
5.3%
P 11469
 
4.9%
L 11022
 
4.7%
D 10754
 
4.6%
E 10237
 
4.3%
Other values (15) 56195
23.8%
Decimal Number
ValueCountFrequency (%)
1 579
77.4%
2 169
 
22.6%
Space Separator
ValueCountFrequency (%)
164406
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2160
100.0%
Other Punctuation
ValueCountFrequency (%)
% 748
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1249990
88.1%
Common 168062
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 147096
 
11.8%
o 92467
 
7.4%
a 92276
 
7.4%
n 74074
 
5.9%
i 69460
 
5.6%
t 68733
 
5.5%
r 67352
 
5.4%
l 59835
 
4.8%
s 57796
 
4.6%
d 40545
 
3.2%
Other values (41) 480356
38.4%
Common
ValueCountFrequency (%)
164406
97.8%
- 2160
 
1.3%
% 748
 
0.4%
1 579
 
0.3%
2 169
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1418052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
164406
 
11.6%
e 147096
 
10.4%
o 92467
 
6.5%
a 92276
 
6.5%
n 74074
 
5.2%
i 69460
 
4.9%
t 68733
 
4.8%
r 67352
 
4.7%
l 59835
 
4.2%
s 57796
 
4.1%
Other values (46) 524557
37.0%

List Price
Real number (ℝ)

HIGH CORRELATION 

Distinct1062
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean514.70126
Minimum0
Maximum2760.7
Zeros295
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size510.1 KiB
2023-07-10T18:24:39.519626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36.69
Q1181.56
median325.19
Q3803.86
95-th percentile1431.23
Maximum2760.7
Range2760.7
Interquartile range (IQR)622.3

Descriptive statistics

Standard deviation449.18811
Coefficient of variation (CV)0.87271615
Kurtosis0.012496642
Mean514.70126
Median Absolute Deviation (MAD)217.35
Skewness1.0054596
Sum33600213
Variance201769.95
MonotonicityNot monotonic
2023-07-10T18:24:39.650570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
298 1508
 
2.3%
1431.23 1426
 
2.2%
966.44 1192
 
1.8%
1275.1 1126
 
1.7%
192.34 1041
 
1.6%
1627.84 1035
 
1.6%
157.76 988
 
1.5%
1084.61 975
 
1.5%
181.44 893
 
1.4%
412.03 892
 
1.4%
Other values (1052) 54205
83.0%
ValueCountFrequency (%)
0 295
0.5%
0.3929 150
0.2%
0.4 21
 
< 0.1%
0.405 25
 
< 0.1%
0.41 10
 
< 0.1%
0.445 6
 
< 0.1%
0.52 1
 
< 0.1%
0.61 4
 
< 0.1%
1.6236 2
 
< 0.1%
1.8711 9
 
< 0.1%
ValueCountFrequency (%)
2760.7 12
 
< 0.1%
2291.4 7
 
< 0.1%
2267 10
 
< 0.1%
1975 113
0.2%
1920 61
0.1%
1880 19
 
< 0.1%
1759.4 45
 
0.1%
1731.4 35
 
0.1%
1691.4 12
 
< 0.1%
1688.13 150
0.2%

Sales Amount
Real number (ℝ)

HIGH CORRELATION 

Distinct17895
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2852.0055
Minimum200.01
Maximum555376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.1 KiB
2023-07-10T18:24:39.787034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum200.01
5-th percentile215.78
Q1308.38
median553.94
Q31279.96
95-th percentile8777.79
Maximum555376
Range555175.99
Interquartile range (IQR)971.58

Descriptive statistics

Standard deviation15164.456
Coefficient of variation (CV)5.3171202
Kurtosis478.90724
Mean2852.0055
Median Absolute Deviation (MAD)292.92
Skewness18.578552
Sum1.8618177 × 108
Variance2.2996072 × 108
MonotonicityNot monotonic
2023-07-10T18:24:39.918612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
784.97 115
 
0.2%
817.68 115
 
0.2%
294.72 110
 
0.2%
307 104
 
0.2%
597.14 102
 
0.2%
622.02 101
 
0.2%
824.39 100
 
0.2%
791.41 99
 
0.2%
401.16 95
 
0.1%
204.66 92
 
0.1%
Other values (17885) 64248
98.4%
ValueCountFrequency (%)
200.01 6
< 0.1%
200.06 6
< 0.1%
200.08 1
 
< 0.1%
200.14 3
< 0.1%
200.15 5
< 0.1%
200.19 7
< 0.1%
200.21 1
 
< 0.1%
200.3 3
< 0.1%
200.36 1
 
< 0.1%
200.37 6
< 0.1%
ValueCountFrequency (%)
555376 1
 
< 0.1%
539200 5
< 0.1%
517632 5
< 0.1%
472069.6 2
 
< 0.1%
458320 5
< 0.1%
439987.2 5
< 0.1%
310156.07 1
 
< 0.1%
301122.4 2
 
< 0.1%
297240 1
 
< 0.1%
289077.5 2
 
< 0.1%

Sales Amount Based on List Price
Real number (ℝ)

HIGH CORRELATION 

Distinct4060
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4707.5457
Minimum0
Maximum632610.16
Zeros295
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size510.1 KiB
2023-07-10T18:24:40.061776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile390
Q1561.04
median998.16
Q32315.04
95-th percentile16425.12
Maximum632610.16
Range632610.16
Interquartile range (IQR)1754

Descriptive statistics

Standard deviation20696.594
Coefficient of variation (CV)4.3964722
Kurtosis278.71143
Mean4707.5457
Median Absolute Deviation (MAD)524.88
Skewness14.074724
Sum3.0731329 × 108
Variance4.2834901 × 108
MonotonicityNot monotonic
2023-07-10T18:24:40.200192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1431.23 590
 
0.9%
1627.84 530
 
0.8%
803.86 498
 
0.8%
596 448
 
0.7%
1254.1899 418
 
0.6%
966.44 376
 
0.6%
439.7 372
 
0.6%
507.75 363
 
0.6%
767.75 348
 
0.5%
939.57 343
 
0.5%
Other values (4050) 60995
93.4%
ValueCountFrequency (%)
0 295
0.5%
194 2
 
< 0.1%
195.61 1
 
< 0.1%
198.396 1
 
< 0.1%
198.63 1
 
< 0.1%
200.7 8
 
< 0.1%
200.8 1
 
< 0.1%
201.69 3
 
< 0.1%
202.14 1
 
< 0.1%
202.6 1
 
< 0.1%
ValueCountFrequency (%)
632610.16 5
< 0.1%
624453.75 2
 
< 0.1%
539200 11
< 0.1%
458320 12
< 0.1%
391924.7232 5
< 0.1%
387395 8
< 0.1%
348655.5 2
 
< 0.1%
332196.405 2
 
< 0.1%
330708.3792 5
< 0.1%
310273.7392 2
 
< 0.1%

Sales Cost Amount
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5513
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1661.0047
Minimum0
Maximum366576
Zeros348
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size510.1 KiB
2023-07-10T18:24:40.346893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile85.36
Q1167.79
median304.5
Q3687.4
95-th percentile4946.11
Maximum366576
Range366576
Interquartile range (IQR)519.61

Descriptive statistics

Standard deviation9556.5562
Coefficient of variation (CV)5.7534794
Kurtosis614.26709
Mean1661.0047
Median Absolute Deviation (MAD)171.25
Skewness21.010786
Sum1.0843205 × 108
Variance91327767
MonotonicityNot monotonic
2023-07-10T18:24:40.485915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
449.69 534
 
0.8%
475.75 457
 
0.7%
0 348
 
0.5%
134.67 305
 
0.5%
162.89 289
 
0.4%
205.72 253
 
0.4%
159.14 242
 
0.4%
16718.08 234
 
0.4%
546.44 231
 
0.4%
344.28 229
 
0.4%
Other values (5503) 62159
95.2%
ValueCountFrequency (%)
0 348
0.5%
12.97 2
 
< 0.1%
19.55 4
 
< 0.1%
20.8 6
 
< 0.1%
26 1
 
< 0.1%
31.19 4
 
< 0.1%
33.97 3
 
< 0.1%
35.48 2
 
< 0.1%
35.54 5
 
< 0.1%
36.03 1
 
< 0.1%
ValueCountFrequency (%)
366576 7
 
< 0.1%
353292.8 4
 
< 0.1%
311589.6 12
 
< 0.1%
185048.85 2
 
< 0.1%
161446.35 5
 
< 0.1%
157412.85 2
 
< 0.1%
153635.03 5
 
< 0.1%
146630.4 4
 
< 0.1%
141265.56 36
0.1%
137736.24 4
 
< 0.1%

Sales Margin Amount
Real number (ℝ)

HIGH CORRELATION 

Distinct21295
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1191.0008
Minimum-3932.93
Maximum188800
Zeros3
Zeros (%)< 0.1%
Negative576
Negative (%)0.9%
Memory size510.1 KiB
2023-07-10T18:24:40.637837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3932.93
5-th percentile61.54
Q1129.95
median246.49
Q3579.32
95-th percentile3824.43
Maximum188800
Range192732.93
Interquartile range (IQR)449.37

Descriptive statistics

Standard deviation5860.8134
Coefficient of variation (CV)4.9209148
Kurtosis324.93262
Mean1191.0008
Median Absolute Deviation (MAD)140.28
Skewness15.571533
Sum77749723
Variance34349134
MonotonicityNot monotonic
2023-07-10T18:24:40.780799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
374.7 93
 
0.1%
5317.17 88
 
0.1%
6235.31 87
 
0.1%
341.72 84
 
0.1%
15.32 69
 
0.1%
52.8 67
 
0.1%
37.08 67
 
0.1%
431.88 64
 
0.1%
464.59 64
 
0.1%
24.53 63
 
0.1%
Other values (21285) 64535
98.9%
ValueCountFrequency (%)
-3932.93 1
< 0.1%
-3764.4 2
< 0.1%
-3673.68 2
< 0.1%
-3608.81 1
< 0.1%
-3414.01 2
< 0.1%
-3132.65 2
< 0.1%
-2533.97 2
< 0.1%
-2508.21 2
< 0.1%
-2488.89 1
< 0.1%
-2103.04 2
< 0.1%
ValueCountFrequency (%)
188800 1
 
< 0.1%
185907.2 2
< 0.1%
172624 3
< 0.1%
164339.2 2
< 0.1%
160480 2
< 0.1%
156773.4 1
 
< 0.1%
156521.04 1
 
< 0.1%
151056 3
< 0.1%
148401.6 3
< 0.1%
147487.37 2
< 0.1%

Sales Price
Real number (ℝ)

HIGH CORRELATION 

Distinct14789
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.69685
Minimum0.33734118
Maximum6035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.1 KiB
2023-07-10T18:24:41.071791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.33734118
5-th percentile22.425556
Q1100.07
median183.759
Q3448.22
95-th percentile789.66
Maximum6035
Range6034.6627
Interquartile range (IQR)348.15

Descriptive statistics

Standard deviation252.02973
Coefficient of variation (CV)0.88837691
Kurtosis6.8826841
Mean283.69685
Median Absolute Deviation (MAD)116.471
Skewness1.4189862
Sum18520014
Variance63518.984
MonotonicityNot monotonic
2023-07-10T18:24:41.207540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140.43 191
 
0.3%
817.68 189
 
0.3%
133.41 181
 
0.3%
824.39 138
 
0.2%
783.17 138
 
0.2%
23.47 136
 
0.2%
82.87333333 125
 
0.2%
230.98 120
 
0.2%
221.04 120
 
0.2%
230.25 120
 
0.2%
Other values (14779) 63823
97.8%
ValueCountFrequency (%)
0.3373411765 2
 
< 0.1%
0.3514 1
 
< 0.1%
0.3619411765 1
 
< 0.1%
0.37718 67
0.1%
0.384 9
 
< 0.1%
0.3888 12
 
< 0.1%
0.3929 67
0.1%
0.3936 5
 
< 0.1%
0.4 9
 
< 0.1%
0.40469 16
 
< 0.1%
ValueCountFrequency (%)
6035 1
< 0.1%
3748 2
< 0.1%
3233.36 1
< 0.1%
3009.86 1
< 0.1%
3003.41 1
< 0.1%
2823 1
< 0.1%
2753.32 1
< 0.1%
2560 1
< 0.1%
2540.17 1
< 0.1%
2360.1 1
< 0.1%

Sales Quantity
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct280
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.085017
Minimum0
Maximum16000
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size510.1 KiB
2023-07-10T18:24:41.360929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q38
95-th percentile86
Maximum16000
Range16000
Interquartile range (IQR)6

Descriptive statistics

Standard deviation429.66505
Coefficient of variation (CV)9.5301072
Kurtosis649.74753
Mean45.085017
Median Absolute Deviation (MAD)2
Skewness23.007396
Sum2943195
Variance184612.05
MonotonicityNot monotonic
2023-07-10T18:24:41.506537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15264
23.4%
2 13466
20.6%
3 7056
10.8%
4 4973
 
7.6%
5 3519
 
5.4%
6 3061
 
4.7%
10 2596
 
4.0%
8 1460
 
2.2%
12 1314
 
2.0%
20 1034
 
1.6%
Other values (270) 11538
17.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 15264
23.4%
2 13466
20.6%
3 7056
10.8%
4 4973
 
7.6%
5 3519
 
5.4%
6 3061
 
4.7%
7 711
 
1.1%
8 1460
 
2.2%
9 453
 
0.7%
ValueCountFrequency (%)
16000 11
 
< 0.1%
13600 12
 
< 0.1%
9504 7
 
< 0.1%
8316 40
0.1%
7128 21
< 0.1%
7126 2
 
< 0.1%
6480 2
 
< 0.1%
6400 4
 
< 0.1%
5834 4
 
< 0.1%
4752 13
 
< 0.1%

Year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size510.1 KiB
2017
30574 
2019
28021 
2018
6686 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters261124
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 30574
46.8%
2019 28021
42.9%
2018 6686
 
10.2%

Length

2023-07-10T18:24:41.866067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T18:24:42.007090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2017 30574
46.8%
2019 28021
42.9%
2018 6686
 
10.2%

Most occurring characters

ValueCountFrequency (%)
2 65281
25.0%
0 65281
25.0%
1 65281
25.0%
7 30574
11.7%
9 28021
10.7%
8 6686
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 261124
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 65281
25.0%
0 65281
25.0%
1 65281
25.0%
7 30574
11.7%
9 28021
10.7%
8 6686
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 261124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 65281
25.0%
0 65281
25.0%
1 65281
25.0%
7 30574
11.7%
9 28021
10.7%
8 6686
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 261124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 65281
25.0%
0 65281
25.0%
1 65281
25.0%
7 30574
11.7%
9 28021
10.7%
8 6686
 
2.6%

Month
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size510.1 KiB
Mar
7308 
Feb
6556 
Jan
6066 
Dec
5645 
Sep
5555 
Other values (7)
34151 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters195843
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJan
2nd rowJan
3rd rowJan
4th rowJan
5th rowJan

Common Values

ValueCountFrequency (%)
Mar 7308
11.2%
Feb 6556
10.0%
Jan 6066
9.3%
Dec 5645
8.6%
Sep 5555
8.5%
Jun 5376
8.2%
Oct 5250
8.0%
Nov 5247
8.0%
May 5167
7.9%
Aug 4738
7.3%
Other values (2) 8373
12.8%

Length

2023-07-10T18:24:42.123527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mar 7308
11.2%
feb 6556
10.0%
jan 6066
9.3%
dec 5645
8.6%
sep 5555
8.5%
jun 5376
8.2%
oct 5250
8.0%
nov 5247
8.0%
may 5167
7.9%
aug 4738
7.3%
Other values (2) 8373
12.8%

Most occurring characters

ValueCountFrequency (%)
a 18541
 
9.5%
e 17756
 
9.1%
J 15838
 
8.1%
u 14510
 
7.4%
M 12475
 
6.4%
n 11442
 
5.8%
r 11285
 
5.8%
c 10895
 
5.6%
p 9532
 
4.9%
A 8715
 
4.4%
Other values (12) 64854
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 130562
66.7%
Uppercase Letter 65281
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 18541
14.2%
e 17756
13.6%
u 14510
11.1%
n 11442
8.8%
r 11285
8.6%
c 10895
8.3%
p 9532
7.3%
b 6556
 
5.0%
t 5250
 
4.0%
o 5247
 
4.0%
Other values (4) 19548
15.0%
Uppercase Letter
ValueCountFrequency (%)
J 15838
24.3%
M 12475
19.1%
A 8715
13.3%
F 6556
10.0%
D 5645
 
8.6%
S 5555
 
8.5%
O 5250
 
8.0%
N 5247
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 195843
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 18541
 
9.5%
e 17756
 
9.1%
J 15838
 
8.1%
u 14510
 
7.4%
M 12475
 
6.4%
n 11442
 
5.8%
r 11285
 
5.8%
c 10895
 
5.6%
p 9532
 
4.9%
A 8715
 
4.4%
Other values (12) 64854
33.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 195843
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 18541
 
9.5%
e 17756
 
9.1%
J 15838
 
8.1%
u 14510
 
7.4%
M 12475
 
6.4%
n 11442
 
5.8%
r 11285
 
5.8%
c 10895
 
5.6%
p 9532
 
4.9%
A 8715
 
4.4%
Other values (12) 64854
33.1%

Interactions

2023-07-10T18:24:36.465707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:26.524891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:27.715584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:28.905180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:30.067296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:31.348237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:32.508935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:33.682576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:35.138148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:36.597819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:26.658792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:27.842195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:29.037302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:30.191744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:31.481251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:32.639466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:33.832895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:35.268260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:36.723839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:26.793190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:27.964169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:29.167089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:30.313409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:31.603254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:32.765269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:33.960204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:35.391721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:36.849693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:26.914371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:28.096462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:29.295249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:30.432966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:31.727112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:32.891984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:34.175755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:35.513834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:36.979380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:27.041723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:28.225610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:29.423403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:30.550931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:31.847241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:33.016043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:34.399351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:35.634367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:37.110523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:27.176763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:28.353433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:29.551433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:30.826465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:31.974817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:33.146329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:34.596083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:35.774002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:37.250957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:27.310434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:28.494991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:29.681683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:30.959931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:32.108001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:33.278314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:34.737021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:35.903943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:37.396873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:27.441456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:28.633757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:29.813231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:31.089077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:32.241348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:33.412260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:34.870656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:36.209878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:37.532019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:27.568897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:28.763556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:29.936588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:31.213411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:32.368947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:33.544617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:34.999484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T18:24:36.332646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-10T18:24:42.230936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
CustKeyDiscount AmountList PriceSales AmountSales Amount Based on List PriceSales Cost AmountSales Margin AmountSales PriceSales QuantityYearMonth
CustKey1.000-0.016-0.0080.0150.0060.0190.011-0.0040.0110.1710.048
Discount Amount-0.0161.0000.4380.8230.8960.7830.7310.3670.3800.0000.015
List Price-0.0080.4381.0000.3560.3830.3890.2850.977-0.5420.0050.023
Sales Amount0.0150.8230.3561.0000.9670.9300.8900.3580.4840.0110.006
Sales Amount Based on List Price0.0060.8960.3830.9671.0000.9130.8590.3460.4760.0000.008
Sales Cost Amount0.0190.7830.3890.9300.9131.0000.7060.3730.4230.0000.005
Sales Margin Amount0.0110.7310.2850.8900.8590.7061.0000.3000.4740.0120.005
Sales Price-0.0040.3670.9770.3580.3460.3730.3001.000-0.5670.0240.011
Sales Quantity0.0110.380-0.5420.4840.4760.4230.474-0.5671.0000.0000.004
Year0.1710.0000.0050.0110.0000.0000.0120.0240.0001.0000.370
Month0.0480.0150.0230.0060.0080.0050.0050.0110.0040.3701.000

Missing values

2023-07-10T18:24:37.732893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-10T18:24:37.994959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CustKeyDiscount AmountItemList PriceSales AmountSales Amount Based on List PriceSales Cost AmountSales Margin AmountSales PriceSales QuantityYearMonth
010000460158.04Nationeel Salted Pretzels181.44204.84362.8859.73145.11102.42000022017Jan
110000460420.90Ebony Prepared Salad966.44545.54966.44278.38267.16545.54000012017Jan
210000460663.41Thresher Spicy Mints507.75859.841523.25488.68371.16286.61333332017Jan
310000460179.45Big Time Frozen Cheese Pizza412.03232.58412.0384.23148.35232.58000012017Jan
4100004601516.52Red Spade Turkey Hot Dogs1160.701965.583482.10957.151008.43655.19333332017Jan
510000460274.83Ebony Canned Peanuts157.76356.21631.04237.28118.9389.05250042017Jan
610004302495.15Even Better Low Fat String Cheese561.04626.931122.08395.98230.95313.46500022017Jan
710006862232.25Even Better String Cheese263.15294.05526.30203.0591.00147.02500022017Jan
810006862247.58Even Better Low Fat String Cheese561.04313.46561.04197.99115.47313.46000012017Jan
910006862330.09Washington Cranberry Juice187.01417.95748.04211.87206.08104.48750042017Jan
CustKeyDiscount AmountItemList PriceSales AmountSales Amount Based on List PriceSales Cost AmountSales Margin AmountSales PriceSales QuantityYearMonth
6527110023511489.0600BBB Best Apple Butter515.1400541.221030.2800307.80233.42270.61000022019Dec
6527210023511684.2800Walrus Light Beer288.3100757.271441.5500465.89291.38151.45400052019Dec
6527310023793917.5200Ebony Prepared Salad966.44001015.361932.8800544.55470.81507.68000022019Dec
65274100237932662.4500Moms Sliced Ham101.00002387.555050.00001467.83919.7247.751000502019Dec
6527510026081397.8448Better Rice Soup38.55069548.219946.05484826.774721.4437.0085662582019Dec
6527610026081272.5230Better Fancy Canned Sardines39.15456540.366812.88302969.783570.5837.5882761742019Dec
6527710026081548.2520Monarch Spaghetti40.312813158.1013706.35206424.276733.8338.7002943402019Dec
652781002608126.2588Moms Cole Slaw27.3537630.23656.4888421.22209.0126.259583242019Dec
6527910026081447.6400Swell Canned Peaches14.256010743.3211190.96006258.024485.3013.6857587852019Dec
652801002660615.7200Walrus Chardonnay0.3929377.18392.9000340.1037.080.37718010002019Dec

Duplicate rows

Most frequently occurring

CustKeyDiscount AmountItemList PriceSales AmountSales Amount Based on List PriceSales Cost AmountSales Margin AmountSales PriceSales QuantityYearMonth# duplicates
309310025552601.9033Washington Apple Drink1419.5833817.681419.5833353.09464.59817.6812017Oct21
85010013080634.6133Washington Apple Drink1419.5833784.971419.5833353.09431.88784.9712019Dec20
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